Trump Uses the 'Language of War' on Twitter and It May Have Helped Him Beat His Republican Rivals, Study Finds

As the U.S. heads into election year, researchers are taking a look at past presidential campaigns and the effectiveness of strategies adopted by former candidates—specifically, the social media approach of Republican hopefuls during the primaries back in 2016.

Of 12 main candidates, only two embraced a system that appropriated the "language of war," that researchers at the University at Buffalo and Georgia State University refer to as "strategy." One of those candidates was Donald Trump. The other was John Kasich, the last person to drop out of the Republican primaries, leaving Trump the party's presumptive nominee for 2016.

Researchers writing in the International Journal of Communications analyzed all of the GOP candidates' tweets posted during the primary stages of the 2016 presidential campaign—more than 22,000, in total—using a topic network designed to work with big data.

"Twitter gives candidates more control and agency over their message than the traditional mass media—yet we know little about what politicians do with this power," Yotam Ophir, an assistant professor of communication at the University at Buffalo, said in a statement.

Broadly, they could break down the "framing" of the candidates' social media messages into two categories: "issues" versus "strategy." The former, adopted by most candidates, primarily involves using Twitter as a platform to discuss and promote policy and decision-making as well as a place to identify and solve problems.

The latter, however, appropriates the language of war, the researchers said. It's highly competitive and largely ignores issue-based messages in favor of personal attacks and a focus on performance, style and success in the polls.

The study's authors point to previous research that suggests the use of strategy frames "draw the audience's attention to the motivation of the people depicted." This, so the argument goes, has the effect of encouraging cynicism towards individual politicians and politics at large by making voters feel a politician's words are based on self-interest and ambition for electoral success rather than their genuine opinion.

Ophir and colleagues found issue frames performed best close to a television debate but the strategy frame was more successful as the day of voting loomed. While there will have been a variety of factors that led to the result of the 2016 elections, it is interesting that, ultimately, the two candidates using the strategy frame were the last two in the race.

As well as providing an intriguing insight into the social media preferences (and successes) of the Republican nominees, the study highlights the potential of topic networks when it comes to analyzing patterns in large quantities of tweets. Previous studies might have used a sample of 100 tweets, making it far more manageable for a human to process but at the cost of being less accurate.

"Humans are good at reading individual texts, but not so good at reading thousands of texts and discerning the patterns," said Ophir.

The other benefit is that it comes without the subjectivity of a human being. We're not telling the network what to find, said Ophir. Instead, it identifies patterns using distribution lists of words that often appear in conjunction.

"Just like you can analyze a social media network and see who is friends with whom, or see communities of friends from work or school, [the network] sees associations and creates clusters of topics," said Ophir. "In this case, it found strategy and issue—on its own and not because we programmed it to look for those two."

This is just the start. Ophir and colleagues want to test their network on a larger scale to better understand the effects of different frames on elections.

That includes the 2020 presidential race.

Correction (11/6/19): This article was updated to correct the name of University at Buffalo.